anjaiah madarapu

Glass-fiber-reinforced polymers' (GFRP), as compared to metallic materials, require more intricate machining. The composite construction experiences delamination as a result of this machining operation. Delamination at the exit and entrance of holes drilled is a significant flaw in composite materials. Superior-drilled holes can be produced by optimizing the drilling process's governing factors. This study’s goal is to maximize the drilling settings using entropy weight-coupled gray relational analysis with fuzzy logic to account for multiple performance factors. Taguchi's L25 5-level orthogonal array is used to increase the accuracy of the results in this study. Feed rate and spindle speed are considered the control variables, and torque, thrust force, and delamination at both the exit and entry are the responses. The results show that drilling performance is enhanced by lower feed rates and elevated spindle speeds. Additionally, the present findings show that feed rate has a stronger influence on drilling hole quality. These results also proved that increasing the number of levels increases the accuracy of the results. Entropy-based gray relational analysis with fuzzy logic using more levels of factors can be effectively used for the optimization of the drilling process.